Computer solutions, and in particular algorithms and processes known as artificial intelligence, are in use to an ever increasing extent by companies wishing to communicate with clients or customers. The main benefit is clear; the cost of implementing an artificial intelligence solution is a fraction of the cost of employing people to perform the same role.
However, there are technical difficulties in implementing such a system based on artificial intelligence. In particular, while for simple queries the system may be relatively efficient, in the case of more complex queries, or ones that have never before been presented to the system, current solutions are inadequate, as time and processing resources will be wasted in attempts to resolve the issues using existing artificial intelligence techniques, which will often end in failure. This leads to a heavy burden on the system in terms of the required memory and processing resources, in addition to a poor rate of user satisfaction. Furthermore, in view of this inefficiency, artificial intelligence solutions based on current technology must generally be designed to cope with a high number of concurrent user queries, leading to complex and costly infrastructures.
There is thus a need for a query response solution that overcomes the above drawbacks.